Nonvolatile Multistates Memories for High-Density Data Storage

Memristor-Based Bionic Tactile Devices: Opening the Door for Next-Generation Artificial Intelligence

Bioinspired tactile devices can effectively mimic and reproduce the functions of the human tactile system, presenting significant potential in the field of next-generation wearable electronics. In particular, memristor-based bionic tactile devices have attracted considerable attention due to their exceptional characteristics of high flexibility, low power consumption, and adaptability. These devices provide advanced wearability and high-precision tactile sensing capabilities, thus emerging as an important research area within bioinspired electronics. This paper delves into the integration of memristors with other sensing and controlling systems and offers a comprehensive analysis of the recent research advancements in memristor-based bionic tactile devices. These advancements incorporate artificial nociceptors and flexible electronic skin (e-skin) into the category of bio-inspired sensors equipped with capabilities for sensing, processing, and responding to stimuli, which are expected to catalyze revolutionary changes in human-computer interaction. Finally, this review discusses the challenges faced by memristor-based bionic tactile devices in terms of material selection, structural design, and sensor signal processing for the development of artificial intelligence. Additionally, it also outlines future research directions and application prospects of these devices, while proposing feasible solutions to address the identified challenges.

Pure ZrO2 Ferroelectric Thin Film for Nonvolatile Memory and Neural Network Computing

The recent discovery of ferroelectricity in pure ZrO2 has drawn much attention, but the information storage and processing performances of ferroelectric ZrO2-based nonvolatile devices remain open for further exploration. Here, a ZrO2 (∼8 nm)-based ferroelectric capacitor using RuO2 oxide electrodes is fabricated, and the ferroelectric orthorhombic phase evolution under electric field cycling is studied. A ferroelectric remnant polarization (2Pr) of >30 μC/cm2, leakage current density of ∼2.79 × 10-8 A/cm2 at 1 MV/cm, and estimated polarization retention of >10 years are achieved. When the ferroelectric capacitor is connected with a transistor, a memory window of ∼0.8 V and eight distinct states can be obtained in such a ferroelectric field-effect transistor (FeFET). Through the conductance manipulation of the FeFET, a high object image recognition accuracy of ∼93.32% is achieved on the basis of the CIFAR-10 dataset in the convolutional neural network (CNN) simulation, which is close to the result of ∼94.20% obtained by floating-point-based CNN software. These results demonstrate the potential of ferroelectric ZrO2 devices for nonvolatile memory and artificial neural network computing.

Control-Etched Ti3C2Tx MXene Nanosheets for a Low-Voltage-Operating Flexible Memristor for Efficient Neuromorphic Computation

Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti3C2Tx MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti3C2Tx) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti3C2Tx)-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust ∼103 ON/OFF ratio, high endurance of ∼104 cycles, multilevel resistance states, and long data retention measured up to ∼106 s. High mechanical stability up to ∼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti3C2Tx MXene memristor is employed to mimic the biological synapse by measuring the learning-forgetting pattern for ∼104 cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb's Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of ∼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.

Reduction-Induced Magnetic Behavior in LaFeO3-δ Thin Films

The effect of oxygen reduction on the magnetic properties of LaFeO3-δ (LFO) thin films was studied to better understand the viability of LFO as a candidate for magnetoionic memory. Differences in the amount of oxygen lost by LFO and its magnetic behavior were observed in nominally identical LFO films grown on substrates prepared using different common methods. In an LFO film grown on as-received SrTiO3 (STO) substrate, the original perovskite film structure was preserved following reduction, and remnant magnetization was only seen at low temperatures. In a LFO film grown on annealed STO, the LFO lost significantly more oxygen and the microstructure decomposed into La- and Fe-rich regions with remnant magnetization that persisted up to room temperature. These results demonstrate an ability to access multiple, distinct magnetic states via oxygen reduction in the same starting material and suggest LFO may be a suitable materials platform for nonvolatile multistate memory.

Self-Assembly of Delta-Formamidinium Lead Iodide Nanoparticles to Nanorods: Study of Memristor Properties and Resistive Switching Mechanism

In the quest for advanced memristor technologies, this study introduces the synthesis of delta-formamidinium lead iodide (δ-FAPbI3 ) nanoparticles (NPs) and their self-assembly into nanorods (NRs). The formation of these NRs is facilitated by iodide vacancies, promoting the fusion of individual NPs at higher concentrations. Notably, these NRs exhibit robust stability under ambient conditions, a distinctive advantage attributed to the presence of capping ligands and a crystal lattice structured around face-sharing octahedra. When employed as the active layer in resistive random-access memory devices, these NRs demonstrate exceptional bipolar switching properties. A remarkable on/off ratio (105 ) is achieved, surpassing the performances of previously reported low-dimensional perovskite derivatives and α-FAPbI3 NP-based devices. This enhanced performance is attributed to the low off-state current owing to the reduced number of halide vacancies, intrinsic low dimensionality, and the parallel alignment of NRs on the FTO substrate. This study not only provides significant insights into the development of superior materials for memristor applications but also opens new avenues for exploring low-dimensional perovskite derivatives in advanced electronic devices.

Read Operation Mechanism of Feedback Field-Effect Transistors with Quasi-Nonvolatile Memory States

In this study, the read operation of feedback field-effect transistors (FBFETs) with quasi-nonvolatile memory states was analyzed using a device simulator. For FBFETs, write pulses of 40 ns formed potential barriers in their channels, and charge carriers were accumulated (depleted) in these channels, generating the memory state "State 1 (State 0)". Read pulses of 40 ns read these states with a retention time of 3 s, and the potential barrier formation and carrier accumulation were influenced by these read pulses. The potential barriers were analyzed, using junction voltage and current density to explore the memory states. Moreover, FBFETs exhibited nondestructive readout characteristics during the read operation, which depended on the read voltage and pulse width.

Giant Electroresistance in Ferroelectric Tunnel Junctions via High-Throughput Designs: Toward High-Performance Neuromorphic Computing

Ferroelectric tunnel junctions (FTJs) have been regarded as one of the most promising candidates for next-generation devices for data storage and neuromorphic computing owing to their advantages such as fast operation speed, low energy consumption, convenient 3D stack ability, etc. Here, dramatically different from the conventional engineering approaches, we have developed a tunnel barrier decoration strategy to improve the ON/OFF ratio, where the ultrathin SrTiO3 (STO) dielectric layers are periodically mounted onto the BaTiO3 (BTO) ferroelectric tunnel layer using the high-throughput technique. The inserted STO enhances the local tetragonality of the BTO, resulting in a strengthened ferroelectricity in the tunnel layer, which greatly improves the OFF state and reduces the ON state. Combined with the optimized oxygen migration, which can further manipulate the tunneling barrier, a record-high ON/OFF ratio of ∼108 has been achieved. Furthermore, utilizing these FTJ-based artificial synapses, an artificial neural network has been simulated via back-propagation algorithms, and a classification accuracy as high as 92% has been achieved. This study screens out the prominent FTJ by the high-throughput technique, advancing the tunnel layer decoration at the atomic level in the FTJ design and offering a fundamental understanding of the multimechanisms in the tunnel barrier.

Multivalued Memory via Freezing of Super-Hard Magnetic Domains in a Quasi 2D-Magnet

The design of high-density non-volatile memories is a long-standing dream, limited by conventional storage "0" or "1" bits. An alternative paradigm exists in which regions within candidate materials can be magnetized to intermediate values between the saturation limits. In principle, this paves the way to multivalued bits, vastly increasing storage density. Single-molecule magnets, are good examples offering transitions between intramolecular quantum levels, but require ultra-low temperatures and limited relaxation time between magnetization states. It is showed here that the quasi 2D-Ising compound BaFe2 (PO4 )2 overcomes these limitations. The combination of giant magneto-crystalline anisotropy, strong ferromagnetic exchange, and strong intrinsic pinning creates remarkably narrow magnetic domain walls, collectively freezing under Tf ≈15 K. This results in a transition from a soft to a super-hard magnet (coercive force > 14 T). Any magnetization can then be printed and robustly protected from external fields with an energy barrier >9T at 2 K.

Ultralow Power and Shifting-Discretized Magnetic Racetrack Memory Device Driven by Chirality Switching and Spin Current

Magnetic racetrack memory has significantly evolved and developed since its first experimental verification and is considered one of the most promising candidates for future high-density on-chip solid-state memory. However, both the lack of a fast and precise magnetic domain wall (DW) shifting mechanism and the required extremely high DW motion (DWM) driving current make the racetrack difficult to commercialize. Here, we propose a method for coherent DWM that is free from the above issues, which is driven by chirality switching (CS) and an ultralow spin-orbit-torque (SOT) current. The CS, as the driving force of DWM, is achieved by the sign change of the Dzyaloshinskii-Moriya interaction, which is further induced by a ferroelectric switching voltage. The SOT is used to break the symmetry when the magnetic moment is rotated in the Bloch direction. We numerically investigate the underlying principle and the effect of key parameters on the DWM by micromagnetic simulations. Under the CS mechanism, a fast (∼102 m/s), ultralow energy (∼5 attoJoule), and precisely discretized DWM can be achieved. Considering that skyrmions with topological protection and smaller size are also promising for future racetracks, we similarly evaluate the feasibility of applying such a CS mechanism to a skyrmion. However, we find that the CS causes it to "breathe" instead of moving. Our results demonstrate that the CS strategy is suitable for future DW racetrack memory with ultralow power consumption and discretized DWM.

Highly degenerate 2D ferroelectricity in pore decorated covalent/metal organic frameworks

Two-dimensional (2D) ferroelectricity, a fundamental concept in low-dimensional physics, serves as the basis of non-volatile information storage and various electronic devices. Conventional 2D ferroelectric (FE) materials are usually two-fold degenerate, meaning that they can only store two logical states. In order to break such limitation, a new concept of highly degenerate ferroelectricity with multiple FE states (more than 2) coexisting in a single 2D material is proposed. This is obtained through the asymmetrical decoration of porous covalent/metal organic frameworks (COFs/MOFs). Using first-principles calculations and Monte Carlo (MC) simulations, Li-decorated 2D Cr(pyz)₂ is systematically explored as a prototype of highly degenerate 2D FE materials. We show that 2D FE Li_0.5Cr(pyz)₂ and LiCr(pyz)₂ are four-fold and eight-fold degenerate, respectively, with sizable spontaneous electric polarization that can be switched across low transition barriers. In particular, the coupling between neighbouring electric dipoles in LiCr(pyz)₂ induces novel ferroelectricity-controlled ferroelastic transition and direction-controllable hole transport channels. Moreover, three-fold and six-fold degenerate ferroelectricity is also demonstrated in P-decorated g-C₃N₄ and Ru-decorated C₂N, respectively. Our work presents a general route to obtain highly degenerate 2D ferroelectricity, which goes beyond the two-state paradigm of traditional 2D FE materials and substantially broadens the applications of 2D FE compounds.

Inorganic Halide Perovskite Quantum Dots: A Versatile Nanomaterial Platform for Electronic Applications

Metal halide perovskites have generated significant attention in recent years because of their extraordinary physical properties and photovoltaic performance. Among these, inorganic perovskite quantum dots (QDs) stand out for their prominent merits, such as quantum confinement effects, high photoluminescence quantum yield, and defect-tolerant structures. Additionally, ligand engineering and an all-inorganic composition lead to a robust platform for ambient-stable QD devices. This review presents the state-of-the-art research progress on inorganic perovskite QDs, emphasizing their electronic applications. In detail, the physical properties of inorganic perovskite QDs will be introduced first, followed by a discussion of synthesis methods and growth control. Afterwards, the emerging applications of inorganic perovskite QDs in electronics, including transistors and memories, will be presented. Finally, this review will provide an outlook on potential strategies for advancing inorganic perovskite QD technologies.

Significant Role of Interfacial Spin-Orbit Coupling in the Spin-to-Charge Conversion in Pt/NiFe Heterostructure

For the spin-to-charge conversion (SCC) in heavy metal/ferromagnet (HM/FM) heterostructure, the contribution of interfacial spin-orbit coupling (SOC) remains controversial. Here, we investigate the SCC process of the Pt/NiFe heterostructure by the spin pumping in YIG/Pt/NiFe/IrMn multilayers. Due to the exchange bias of NiFe/IrMn structure, the NiFe magnetization can be switched between magnetically unsaturated and saturated states by opposite resonance fields of YIG layer. The spin-pumping signal is found to decrease significantly when the NiFe magnetization is changed from the saturated state to the unsaturated state. Theoretical analysis indicates that the interfacial spin absorption is enhanced for the above-mentioned NiFe magnetic state change, which results in the increased and decreased spin flow in the Pt layer and across the Pt/NiFe interface, respectively. These results demonstrate that in our case the interfacial SOC effect at the Pt/NiFe interface is dominant over the bulk inverse spin Hall effect in the Pt layer. Our work reveals a significant role of interfacial SOC in the SCC in HM/FM heterostructure, which can promote the development of high-efficiency spintronic devices through interfacial engineering.

Robust 2D MoS2 Artificial Synapse Device Based on a Lithium Silicate Solid Electrolyte for High-Precision Analogue Neuromorphic Computing

High-precision artificial synaptic devices compatible with existing CMOS technology are essential for realizing robust neuromorphic hardware systems with reliable parallel analogue computation beyond the von Neumann serial digital computing architecture. However, critical issues related to reliability and variability, such as nonlinearity and asymmetric weight updates, have been great challenges in the implementation of artificial synaptic devices in practical neuromorphic hardware systems. Herein, a robust three-terminal two-dimensional (2D) MoS₂ artificial synaptic device combined with a lithium silicate (LSO) solid-state electrolyte thin film is proposed. The rationally designed synaptic device exhibits excellent linearity and symmetry upon electrical potentiation and depression, benefiting from the reversible intercalation of Li ions into the MoS₂ channel. In particular, extremely low cycle-to-cycle variations (3.01%) during long-term potentiation and depression processes over 500 pulses are achieved, causing statistical analogue discrete states. Thus, a high classification accuracy of 96.77% (close to the software baseline of 98%) is demonstrated in the Modified National Institute of Standards and Technology (MNIST) simulations. These results provide a future perspective for robust synaptic device architecture of lithium solid-state electrolytes stacked with 2D van der Waals layered channels for high-precision analogue neuromorphic computing systems.

Bipolar Switching Characteristics of Transparent WOX-Based RRAM for Synaptic Application and Neuromorphic Engineering

In this work, we evaluate the resistive switching (RS) and synaptic characteristics of a fully transparent resistive random-access memory (T-RRAM) device based on indium-tin-oxide (ITO) electrodes. Here, we fabricated ITO/WO_X/ITO capacitor structure and incorporated DC-sputtered WO_X as the switching layer between the two ITO electrodes. The device shows approximately 77% (including the glass substrate) of optical transmittance in visible light and exhibits reliable bipolar switching behavior. The current-voltage (I-V) curve is divided into two types: partial and full curves affected by the magnitude of the positive voltage during the reset process. In the partial curve, we confirmed that the retention could be maintained for more than 10⁴ s and the endurance for more than 300 cycles could be stably secured. The switching mechanism based on the formation/rupture of the filament is further explained through the extra oxygen vacancies provided by the ITO electrodes. Finally, we examined the responsive potentiation and depression to check the synaptic characteristics of the device. We believe that the transparent WO_X-based RRAM could be a milestone for neuromorphic devices as well as future non-volatile transparent memory.

Ultra-Stable, Endurable, and Flexible Sb2TexSe3-x Phase Change Devices for Memory Application and Wearable Electronics

Flexible memory and wearable electronics represent an emerging technology, thanks to their reliability, compatibility, and superior performance. Here, an Sb₂Te_xSe_3-x (STSe) phase change material was grown on flexible mica, which not only exhibited superior nature in thermal stability for phase change memory application but also revealed novel function performance in wearable electronics, thanks to its excellent mechanical reliability and endurance. The thermal stability of Sb₂Te₃ was improved obviously with the crystallization temperature elevated 60 K after Se doping, for the enhanced charge localization and stronger bonding energy, which was validated by the Vienna ab initio simulation package calculations. Based on the ultra-stability of STSe, the STSe-based phase change memory shows 65 000 reversible phase change ability. Moreover, the assembled flexible device can show real-time monitoring and recoverability response in sensing human activities in different parts of the body, which proves its effective reusability and potential as wearable electronics. Most importantly, the STSe device presents remarkable working reliability, reflected by excellent endurance over 100 s and long retention over 100 h. These results paved a novel way to utilize STSe phase change materials for flexible memory and wearable electronics with extreme thermal and mechanical stability and brilliant performance.

Reservoir Computing-Based Design of ZnO Memristor-Type Digital Identification Circuits

Reservoir Computing (RC) is a network architecture inspired by biological neural systems that maps time-dimensional input features to a high-dimensional space for computation. The key to hardware implementation of the RC system is whether sufficient reservoir states can be generated. In this paper, a laboratory-prepared zinc oxide (ZnO) memristor is reported and modeled. The device is found to have nonlinear dynamic responses and characteristics of simulating neurosynaptic long-term potentiation (LTP) and long-term depression (LTD). Based on this, a novel two-level RC structure based on the ZnO memristor is proposed. Novel synaptic encoding is used to maintain stress activity based on the characteristics of after-discharge and proneness to fatigue during synaptic transmission. This greatly alleviates the limitations of the self-attenuating characteristic reservoir of the duration and interval of the input signal. This makes the reservoir, in combination with a fully connected neural network, an ideal system for time series classification. The experimental results show that the recognition rate for the complete MNIST dataset is 95.08% when 35 neurons are present as hidden layers while achieving low training consumption.

Tunable Resistive Switching in 2D MXene Ti3C2 Nanosheets for Non-Volatile Memory and Neuromorphic Computing

An artificial synapse is essential for neuromorphic computing which has been expected to overcome the bottleneck of the traditional von-Neumann system. Memristors can work as an artificial synapse owing to their tunable non-volatile resistance states which offer the capabilities of information storage, processing, and computing. In this work, memristors based on two-dimensional (2D) MXene Ti₃C₂ nanosheets sandwiched by Pt electrodes are investigated in terms of resistive switching (RS) characteristics, synaptic functions, and neuromorphic computing. Digital and analog RS behaviors are found to coexist depending on the magnitude of operation voltage. Digital RS behaviors with two resistance states possessing a large switching ratio exceeding 10³ can be achieved under a high operation voltage. Analog RS behaviors with a series of resistance states exhibiting a gradual change can be observed at a relatively low operation voltage. Furthermore, artificial synapses can be implemented based on the memristors with the basic synaptic functions, such as long-term plasticity of long-term potentiation and depression and short-term plasticity of the paired-pulse facilitation and depression. Moreover, the "learning-forgetting" experience is successfully emulated based on the artificial synapses. Also, more importantly, the artificial synapses can construct an artificial neural network to implement image recognition. The coexistence of digital and analog RS behaviors in the 2D Ti₃C₂ nanosheets suggests the potential applications in non-volatile memory and neuromorphic computing, which is expected to facilitate simplifying the manufacturing complexity for complex neutral systems where analog and digital switching is essential for information storage and processing.

Filamentary and Interface-Type Memristors Based on Tantalum Oxide for Energy-Efficient Neuromorphic Hardware

To implement artificial neural networks (ANNs) based on memristor devices, it is essential to secure the linearity and symmetry in weight update characteristics of the memristor, and reliability in the cycle-to-cycle and device-to-device variations. This study experimentally demonstrated and compared the filamentary and interface-type resistive switching (RS) behaviors of tantalum oxide (Ta₂O₅ and TaO₂)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta₂O₅ is a strong candidate for memristor, the filament-type RS behavior of Ta₂O₅ does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-type TaO₂ memristor and compared it to a filament type of Ta₂O₅ memristor to secure the weight update characteristics and reliability. The TaO₂-based interface-type memristor exhibited gradual RS characteristics and area dependency in both high- and low-resistance states. In addition, compared to the filamentary memristor, the RS behaviors of the TaO₂-based interface-type device exhibited higher suitability for the neuromorphic, symmetric, and linear long-term potentiation (LTP) and long-term depression (LTD). These findings suggest better types of memristors for implementing ionic memristor-based ANNs among the two types of RS mechanisms.

Low-Dimensional Metal-Halide Perovskites as High-Performance Materials for Memory Applications

Metal-halide perovskites have drawn profuse attention during the past decade, owing to their excellent electrical and optical properties, facile synthesis, efficient energy conversion, and so on. Meanwhile, the development of information storage technologies and digital communications has fueled the demand for novel semiconductor materials. Low-dimensional perovskites have offered a new force to propel the developments of the memory field due to the excellent physical and electrical properties associated with the reduced dimensionality. In this review, the mechanisms, properties, as well as stability and performance of low-dimensional perovskite memories, involving both molecular-level perovskites and structure-level nanostructures, are comprehensively reviewed. The property-performance correlation is discussed in-depth, aiming to present effective strategies for designing memory devices based on this new class of high-performance materials. Finally, the existing challenges and future opportunities are presented.

Nanomicelles Array for Ultrahigh-Density Data Storage

High-density data storage devices based on organic and polymer materials are currently restricted by two key issues, size limitations and uniformity of memory cells. Herein, one triblock polymer is synthesized by ring-opening metathesis polymerization, where the polymer contains an electron-donor-acceptor (A₁ D) segment, an electron-acceptor (A₂ ) segment, and a hydrophilic segment, that shows ternary memory behavior in a conventional sandwich-type device. The polymers that have monodisperse molecular weight dispersity self-assemble into nanomicelles with a uniform size of 80 nm. Each nanomicelle is composed of an A₁ DA₂ -type hydrophobic core stabilized with a hydrophilic shell. Nanobowls based on conductive oxide are prepared via the template method, wherein the nanomicelles are present as independent nanoscale memory units to produce an array of micelle matrices. Investigations of the resulting nanomemory device using conductive atomic force microscopy show that the micelles exhibit a predominant semiconductor memory behavior. Compared to traditional ternary devices with a memory unit size of ≈1 mm, this innovative fabrication method based on arrayed uniform nanomicelles downscales the size of storage cells to 80 nm. Furthermore, the described system leads to a greatly enhanced storage density (>10⁸ times over the same area), which opens up new paths for further development of ultrahigh-density data storage devices.

Synaptic Segmented Transistor with Improved Linearity by Schottky Junctions and Accelerated Speed by Double-Layered Nitride

Neuromorphic devices have been extensively studied to overcome the limitations of a von Neumann system for artificial intelligence. A synaptic device is one of the most important components in the hardware integration for a neuromorphic system because a number of synaptic devices can be connected to a neuron with compactness as high as possible. Therefore, synaptic devices using silicon-based memory, which are advantageous for a high packing density and mass production due to matured fabrication technologies, have attracted considerable attention. In this study, a segmented transistor devoted to an artificial synapse is proposed for the first time to improve the linearity of the potentiation and depression (P/D). It is a complementary metal oxide semiconductor (CMOS)-compatible device that harnesses both non-ohmic Schottky junctions of the source and drain for improved weight linearity and double-layered nitride for enhanced speed. It shows three distinct and unique segments in drain current-gate voltage transfer characteristics induced by Schottky junctions. In addition, the different stoichiometries of Si_xN_y for a double-layered nitride is utilized as a charge trap layer for boosting the operation speed. This work can bring the industry potentially one step closer to realizing the mass production of hardware-based synaptic devices in the future.

High-Speed Nanoscale Ferroelectric Tunnel Junction for Multilevel Memory and Neural Network Computing

Ferroelectric tunnel junction (FTJ) is one promising candidate for next-generation nonvolatile data storage and neural network computing systems. In this work, the high-performance 50 nm-diameter Au/Ti/PbZr_0.52Ti_0.48O₃ (∼3 nm, (111)-oriented)/Nb:SrTiO₃ (Nb: 0.7 wt %) FTJs are achieved to demonstrate the scaling down capability of FTJ. As a nonvolatile memory, the FTJ shows eight distinct resistance states (3 bits) with a large ON/OFF ratio (>10³), and these states can be switched at a fast speed of 10 ns. Intriguingly, the long-term potentiation/depression and spike timing-dependent plasticity, that is, fundamental functions of biological synapses, can be emulated in the nanoscale FTJ-based artificial synapse. A convolutional neural network (CNN) simulation is then carried out based on the experimental results, and a high recognition accuracy of ∼93.8% on fashion product images is obtained, which is very close to the result of ∼94.4% by a floating-point-based CNN software. In particular, the FTJ-based CNN simulation also exhibits robustness to input image noises. These results indicate the great potential of FTJ for high-density information storage and neural network computing.

High Performance Full-Inorganic Flexible Memristor with Combined Resistance-Switching

Flexible memristors hold great promise for flexible electronics applications but are still lacking of good electrical performance together with mechanical flexibility. Herein, we demonstrate a full-inorganic nanoscale flexible memristor by using free-standing ductile α-Ag₂S films as both a flexible substrate and a functional electrolyte. The device accesses dense multiple-level nonvolatile states with a record high 10⁶ ON/OFF ratio. This exceptional memristor performance is induced by sequential processes of Schottky barrier modification at the contact interface and filament formation inside the electrolyte. In addition, it is crucial to ensure that the cathode junction, where Ag⁺ is reduced to Ag, dominates the total resistance and takes the most of setting bias before the filament formation. Our study provides a comprehensive insight into the resistance-switching mechanism in conductive-bridging memristors and offers a new strategy toward high performance flexible memristors.

A Hierarchically Encoded Data Storage Device with Controlled Transiency

In the era of information explosion, high-security and high-capacity data storage technology attracts more and more attention. Physically transient electronics, a form of electronics that can physically disappear with precisely controlled degradation behaviors, paves the way for secure data storage. Herein, the authors report a silk-based hierarchically encoded data storage device (HEDSD) with controlled transiency. The HEDSD can store electronic, photonic, and optical information simultaneously by synergistically integrating a resistive switching memory (ReRAM), a terahertz metamaterial device, and a diffractive optical element, respectively. These three data storage units have shared materials and structures but diverse encoding mechanisms, which increases the degree of complexity and capacity of stored information. Silk plays an important role as a building material in the HEDSD thanks to its excellent mechanical, optical, and electrical properties and controlled transiency as a naturally extracted protein. By controlling the degradation rate of storage units of the silk-based HEDSD, different degradation modes of the HEDSD, and multilevel information encryption/decryption have been realized. Compared with the conventional memory devices, as-reported silk-based HEDSD can store multilevel complex information and realize multilevel information encryption and decryption, which is highly desirable to fulfill the future demands of secure memory systems and implantable storage devices.

Programmable Spin-Orbit Torque Multistate Memory and Spin Logic Cell

Controllable spin-orbit torque based nonvolatile memory is highly desired for constructing energy efficient reconfigurable logic-in-memory computing suitable for emerging data-intensive applications. Here, we report our exploration of the IrMn/Co/Ru/CoPt/CoO heterojunction as a potential candidate for applications in both multistate memory and programmable spin logic. The studied heterojunction can be programmed into four different magnetic configurations at will by tuning both the in-plane exchange bias at the interface of IrMn and Co layers and the out-of-plane exchange bias at the interface of CoPt and CoO layers. Moreover, on the basis of the controllable exchange bias effect, 10 states of nonvolatile memory and multiple logic-in-memory functions have been demonstrated. Our findings indicate that IrMn/Co/Ru/CoPt/CoO multilayered structures can be used as a building block for next-generation logic-in-memory and multifunctional multidimensional spintronic devices.

Effective Transport Tunnels Achieved by 1,2,4,5-Tetrazine-Induced Intermolecular C-H...N Interaction and Anion Radicals for Stable ReRAM Performance

The D-A structured small-molecule-based resistive random-access memory (ReRAM) device has been well-researched in the last decade, and the switching mechanism was mainly induced by the intramolecular/intermolecular charge transfer processes from the donors to the acceptors. However, in the previous work, some small molecules with pristine electron acceptors in the backbone could still show the typical memory behaviors, of which the switching mechanism is still ambiguous. In this work, two 1,2,4,5-tetrazine based n-type small-molecular isomers, 2-DPTZ and 4-DPTZ, with the same electron acceptor, 1,2,4,5-tetrazine and pyridine, are chosen to investigate the isomeric effects on molecular packing, switching mechanism, and memory performance. Because of the abundant nitrogen atoms with a localized lone pair of electrons in the sp² orbital, 2-DPTZ and 4-DPTZ compounds could self-assemble into a long-range ordered molecular packing through intermolecular C-H...N interactions, affording effective transporting tunnels for charge-carrier transport. As expected, the sandwich-structured ITO/2-DPTZ or 4-DPTZ/Al memory devices both showed binary memory characteristics, with 2-DPTZ based memory devices showing the write once read many times (WORM) memory behavior and 4-DPTZ based memory devices having the negative differential resistance (NDR) memory performance. These distinct ReRAM properties arose from the different morphologies of 2-DPTZ and 4-DPTZ films that were induced by the different packing styles between the adjacent molecules, as confirmed by X-ray diffraction (XRD) and tapping-mode atomic force microscopy (AFM) height images. Most importantly, the switching mechanism was thought to be attributed to the injected electrons that reduced the neutral molecules of 2-DPTZ and 4-DPTZ to their corresponding anion radicals. Thus, this present work helps us better understand the conducting mechanism of small molecules with pristine electron acceptors in the backbone and provides a supplementary guideline for designing multilevel small molecules to match the structure-stacking-property relationship.

Nanofiber Architecture Engineering Implemented by Electrophoretic-Induced Self-Assembly Deposition Technology for Flash-Type Memristors

Electrophoretic deposition (EPD) has been recognized as a promising large-scale film preparation technology for industrial application. Inspired by the conventional EPD method and the crystal diffusion growth strategy, we propose a modified electrophoretic-induced self-assembly deposition (EPAD) technique to control the morphologies of organic functional materials. Here, an ionic-type dye with a conjugated skeleton and strong noncovalent interactions, celestine blue (CB), is chosen as a module molecule for EPAD investigation. As expected, CB molecules can assemble into different nanostructures, dominated by applied voltage, concentration effect, and duration. Compared to a nanopillar layered packing structure formed by the traditional spin-coating method, the EPAD approach can produce a nanofiber structure under a fixed condition of 10 V/10 min. Intriguingly, a memristor device based on a pillar-like nanostructure exhibits WORM-type behavior, while a device based on nanofibers presents Flash memory performance. The assemble process and the memory mechanism are uncovered by molecular dynamics simulations and density-functional theory (DFT) calculations. This work endows the typical EPD technique with a fresh application scenario, where an in-depth study on the growth mechanism of nanofibers and the positive effect of unique morphologies on memristor performance are offered.

Correlative imaging of ferroelectric domain walls

The wealth of properties in functional materials at the nanoscale has attracted tremendous interest over the last decades, spurring the development of ever more precise and ingenious characterization techniques. In ferroelectrics, for instance, scanning probe microscopy based techniques have been used in conjunction with advanced optical methods to probe the structure and properties of nanoscale domain walls, revealing complex behaviours such as chirality, electronic conduction or localised modulation of mechanical response. However, due to the different nature of the characterization methods, only limited and indirect correlation has been achieved between them, even when the same spatial areas were probed. Here, we propose a fast and unbiased analysis method for heterogeneous spatial data sets, enabling quantitative correlative multi-technique studies of functional materials. The method, based on a combination of data stacking, distortion correction, and machine learning, enables a precise mesoscale analysis. When applied to a data set containing scanning probe microscopy piezoresponse and second harmonic generation polarimetry measurements, our workflow reveals behaviours that could not be seen by usual manual analysis, and the origin of which is only explainable by using the quantitative correlation between the two data sets.

Room temperature two terminal tunnel magnetoresistance in a lateral graphene transistor

We investigate the behavior of both pure spin and spin-polarized currents measured with four-probe non-local and two probe local configurations up to room temperature and under an external gate voltage in a lateral graphene transistor, produced using a standard large-scale microfabrication process. The high spin diffusion length of pristine graphene in the channel, measured both directly and by the Hanle effect, and the tuning of the relationship between the electrode resistance areas present in the device architecture allowed us to observe local tunnel magnetoresistance at room temperature, a new finding for this type of device. The results also indicate that while pure spin currents are less sensitive to temperature variations, spin-polarized current switching by an external voltage is more efficient, due to a combination of the Rashba effect and a change in carrier mobility by a Fermi level shift.

Resistive Memory Devices Based on Reticular Materials for Electrical Information Storage

Recently, reticular materials, such as metal-organic frameworks and covalent organic frameworks, have been proposed as an active insulating layer in resistive switching memory systems through their chemically tunable porous structure. A resistive random access memory (RRAM) cell, a digital memristor, is one of the most outstanding emergent memory devices that achieves high-density electrical information storage with variable electrical resistance states between two terminals. The overall design of the RRAM devices comprises an insulating layer sandwiched between two metal electrodes (metal/insulator/metal). RRAM devices with fast switching speeds and enhanced storage density have the potential to be manufactured with excellent scalability owing to their relatively simple device architecture. In this review, recent progress on the development of reticular material-based RRAM devices and the study of their operational mechanisms are reviewed, and new challenges and future perspectives related to reticular material-based RRAM are discussed.

Resistive Switching in HfO2-x/La0.67Sr0.33MnO3 Heterostructures: An Intriguing Case of Low H-Field Susceptibility of an E-Field Controlled Active Interface

High-performance nonvolatile resistive random access memories (ReRAMs) and their small stimuli control are of immense interest for high-speed computation and big-data processing in the emerging Internet of Things (IoT) arena. Here, we examine the resistive switching (RS) behavior in growth-controlled HfO₂/La_0.67Sr_0.33MnO₃ (LSMO) heterostructures and their tunability in a low magnetic field. It is demonstrated that oxygen-deficient HfO₂ films show bipolar switching with a high on/off ratio, stable retention, as well as good endurance owing to the orthorhombic-rich phase constitution and charge (de)trapping-enabled Schottky-type conduction. Most importantly, we have demonstrated that RS can be tuned by a very low externally applied magnetic field (∼0-30 mT). Remarkably, application of a magnetic field of 30 mT causes RS to be fully quenched and frozen in the high resistive state (HRS) even after the removal of the magnetic field. However, the quenched state could be resurrected by applying a higher bias voltage than the one for initial switching. This is argued to be a consequence of the electronically and ionically "active" nature of the HfO_2-x/LSMO interface on both sides and its susceptibility to the electric and low magnetic field effects. This result could pave the way for new designs of interface-engineered high-performance oxitronic ReRAM devices.

Stimuli-Responsive Memristive Materials for Artificial Synapses and Neuromorphic Computing

Neuromorphic computing holds promise for building next-generation intelligent systems in a more energy-efficient way than the conventional von Neumann computing architecture. Memristive hardware, which mimics biological neurons and synapses, offers high-speed operation and low power consumption, enabling energy- and area-efficient, brain-inspired computing. Here, recent advances in memristive materials and strategies that emulate synaptic functions for neuromorphic computing are highlighted. The working principles and characteristics of biological neurons and synapses, which can be mimicked by memristive devices, are presented. Besides device structures and operation with different external stimuli such as electric, magnetic, and optical fields, how memristive materials with a rich variety of underlying physical mechanisms can allow fast, reliable, and low-power neuromorphic applications is also discussed. Finally, device requirements are examined and a perspective on challenges in developing memristive materials for device engineering and computing science is given.

A voltage-pulse-modulated giant magnetoresistance switch with four flexible sensing ranges

This article introduces an innovative technique for achieving a giant magnetoresistance (GMR) switch with an adjustable sensing field range. A spin-valve (SV) patterned into a strip shape is grown on a specific (110)-cut Pb(Mg_1/3Nb_2/3)_0.7Ti_0.3O₃(PMN-PT) substrate. In the process of depositing films, a magnetic easy axis of the free layer in the SV is produced along the [001] direction (thex-axis) of the PMN-PT. This PMN-PT can produce a nonvolatile strain by using a positive voltage pulse. Accordingly, the magnetic moment of the free layer can be modulated to they-axis by the strain-mediated magnetoelectric coupling effect produced in the SV/PMN-PT heterostructure. Furthermore, a negative voltage pulse can release the strain and revert the magnetic moment to the initial [001] direction. The effective field along the [1-10] direction produced by the nonvolatile strain can modulate the easy axis of the free layer, changing it from thex-axis to they-axis. Therefore, large and small switching fields are achieved in a bipolar GMR switch. Furthermore, by applying positive and negative voltage pulses at appropriate moments, two asymmetrical switching field ranges are obtained. Thus, a GMR switch with four adjustable switching field ranges can be obtained. The proposed modulating model is flexible and can meet the requirements of specific and different application systems. The proposed design reveals a great potential for the application to the internet of things and the development of low-power and high-efficient magnetoresistive sensors.

The large perpendicular magnetic anisotropy induced at the Co2FeAl/MgAl2O4interface and tuned with the strain, voltage and charge doping by first principles study

The heterostructures with high perpendicular magnetic anisotropy (PMA) have advantages for the application of the nonvolatile memories with long data retention time and small size. The interface structure and magnetic anisotropy energy (MAE) of Co₂FeAl/MgAl₂O₄heterostructures were studied by first principles calculations. The stable interface atomic arrangement is the Co or FeAl layer located above the equatorial oxygen coordinate in the distorted oxygen octahedrons. The Co-O interface can induce large effective PMA up to 4.54 mJ m^-2, but this structure is a metastable structure. Meanwhile, the effective MAE decreases linearly as the thickness of the ferromagnetic layer increase. The effective MAE for the FeAl-O interface is only 1.3 mJ m^-2, while the maximum thickness of Co₂FeAl layer that maintains the PMA effect is about 1.717 nm. These values are very close to the experimental results. Throughd-orbital-resolved MAE, we confirm that the interface PMA is mainly originated from the hybridization betweendxy,dyzanddz2orbitals of interface 3datoms. In addition, the compressive strain, negative electric field and hole doping can significantly enhance the effective PMA of FeAl-O interface. At the same time, Co-O interface will become the most stable structure by tuning the Mg/Al ratio in the spinel layers. The large effective PMA makes the Co₂FeAl/MgAl₂O₄junction a perfect candidate for the next-generation of non-volatile spintronic devices.

Artificial Synapses Based on Ferroelectric Schottky Barrier Field-Effect Transistors for Neuromorphic Applications

Artificial synapses based on ferroelectric Schottky barrier field-effect transistors (FE-SBFETs) are experimentally demonstrated. The FE-SBFETs employ single-crystalline NiSi₂ contacts with an atomically flat interface to Si and Hf_0.5Zr_0.5O₂ ferroelectric layers on silicon-on-insulator substrates. The ferroelectric polarization switching dynamics gradually modulate the NiSi₂/Si Schottky barriers and the potential of the channel, thus programming the device conductance with input voltage pulses. The short-term synaptic plasticity is characterized in terms of excitatory/inhibitory post-synaptic current (EPSC) and paired-pulse facilitation/depression. The EPSC amplitude shows a linear response to the amplitude of the pre-synaptic spike. Very low energy/spike consumption as small as ∼2 fJ is achieved, demonstrating high energy efficiency. Long-term potentiation/depression results show very high endurance and very small cycle-to-cycle variations (∼1%) after 10⁵ pulse measurements. Furthermore, spike-timing-dependent plasticity is also emulated using the gate voltage pulse as the pre-synaptic spike and the drain voltage pulse as the post-synaptic spikes. These findings indicate that FE-SBFET synapses have high potential for future neuromorphic computing applications.

Down-Scalable and Ultra-fast Memristors with Ultra-high Density Three-Dimensional Arrays of Perovskite Quantum Wires

With strikingly high speed, data retention ability and storage density, resistive RAMs have emerged as a forerunning nonvolatile memory. Here we developed a Re-RAM with ultra-high density array of monocrystalline perovskite quantum wires (QWs) as the switching matrix with a metallic silver conducting pathway. The devices demonstrated high ON/OFF ratio of ∼10⁷ and ultra-fast switching speed of ∼100 ps which is among the fastest in literature. The devices also possess long retention time of over 2 years and record high endurance of ∼6 × 10⁶ cycles for all perovskite Re-RAMs reported. As a concept proof, we have also successfully demonstrated a flexible Re-RAM crossbar array device with a metal-semiconductor-insulator-metal design for sneaky path mitigation, which can store information with long retention. Aggressive downscaling to ∼14 nm lateral dimension produced an ultra-small cell effectively having 76.5 nm² area for single bit storage. Furthermore, the devices also exhibited unique optical programmability among the low resistance states.

Low-Temperature Tunneling Electroresistance in Ferromagnetic Metal/Ferroelectric/Semiconductor Tunnel Junctions

Ferroelectric tunnel junctions (FTJs) as artificial synaptic devices are promising candidates for the building block of nonvolatile data storage devices. However, a small ON/OFF ratio of FTJs limits their application in low-temperature operations. In this work, the influence of quantum interference effects on tunneling electroresistance in the La_0.7Sr_0.3MnO₃/BaTiO₃/Nb:SrTiO₃ (ferromagnetic metal/ferroelectric/semiconductor) FTJ at low temperatures is investigated. The Current-voltage curves are observed in the tunnel junction from 300 to 10 K with a six-unit-cell thick BaTiO₃ film by the ferroelectric polarization effect. First, the ON/OFF current ratio increases from 300 to 30 K due to the increase of polarization in the ferroelectric barrier, and then, it gradually decreases when the temperature drops below 30 K. An anomalous ON/OFF current ratio of ∼10⁵ is obtained at 30 K. The low-temperature tunneling properties in the FTJ are associated with a low-temperature resistivity minimum in the ferromagnetic metal layer by the electron-electron interaction, which increases the La_0.7Sr_0.3MnO₃/BaTiO₃ interface resistance, leading to a higher resistance state and lower I_OFF for the OFF state. As a result, the ON/OFF current ratio is abruptly enhanced at 30 K. Our results emphasize the crucial role of transport properties of La_0.7Sr_0.3MnO₃ in FTJs and pave the way for the design and application of FTJs at low temperatures.

Engineering the Threshold Switching Response of Nb2O5-Based Memristors by Ti Doping

Two terminal metal-oxide-metal devices based on niobium oxide thin films exhibit a wide range of non-linear electrical characteristics that have applications in hardware-based neuromorphic computing. In this study, we compare the threshold-switching and current-controlled negative differential resistance (NDR) characteristics of cross-point devices fabricated from undoped Nb₂O₅ and Ti-doped Nb₂O₅ and show that doping offers an effective means of engineering the device response for particular applications. In particular, doping is shown to improve the device reliability and to provide a means of tuning the threshold and hold voltages, the hysteresis window, and the magnitude of the negative differential resistance. Based on temperature-dependent current-voltage characteristics and lumped-element modelling, these effects are attributed to doping-induced reductions in the device resistance and its rate of change with temperature (i.e., the effective thermal activation energy for conduction). Significantly, these studies also show that a critical activation energy is required for devices to exhibit NDR, with doping providing an effective means of engineering the current-voltage characteristics. These results afford an improved understanding of the physical mechanisms responsible for threshold switching and provide new insights for designing devices for specific applications.